A2A Protocol - How AI Agents Collaborate with Each Other

A2A stands for Agent-to-Agent and denotes a standard through which AI agents communicate and collaborate. While a single agent solves its task on its own, an A2A protocol lets several specialized agents divide tasks among themselves, exchange results, and coordinate - much like departments in a company working together.

The core idea: not a single jack-of-all-trades doing everything, but a group of agents, each with a clearly defined role. One agent plans, another researches, a third checks the result. For this interplay to work, the agents need a common language - and that is exactly what an A2A protocol provides.

What an A2A Protocol Governs

• Discovery: An agent must know which other agents exist and what they can do - comparable to a directory of capabilities.
• Task handover: An agent hands a subtask with context to another and later receives a result back.
• Message format: A uniform format ensures that agents from different vendors understand each other.
• Status and feedback: Is a task still running, finished, or failed? The standard governs how such states are communicated.

Distinction from MCP

A2A and MCP are easily confused but solve different problems and complement each other:

• MCP (agent to tool): The Model Context Protocol connects a single agent with tools and data. An MCP server provides the agent with, say, a CRM or a database.
• A2A (agent to agent): The A2A protocol connects agents with each other so they can work together on a larger task.

In short: MCP gives an agent hands to work with systems; A2A gives a group of agents a common language to collaborate. In practice, complex setups use both - an agent talks to other agents via A2A while accessing its tools via MCP. How an agent addresses tools technically is described by tool calling.

Why Agents Cooperate at All

A group of agents makes sense when a task is too large or too complex for a single AI agent:

• Specialization: Each agent is tailored to its role and delivers better results in its area.
• Division of labor: Subtasks run in parallel instead of one after another.
• Verifiability: A checking agent can review another's work before a result is accepted.

Such a setup is the basis of many agentic workflows, in which several steps and roles converge into an overall result.

Maturity and Context

Agent-to-agent approaches are a young field. There are several proposals and drafts for how agents should find and exchange information, but no single method has become universally established yet. For building multi-agent systems, it is therefore worth relying on open, well-documented approaches and cleanly cutting the responsibilities of the agents.

A2A at Elasticbrains

At Elasticbrains we design multi-agent systems with clear roles and clean communication between the agents - and connect the individual agents to your systems via MCP servers. See how we build the necessary interfaces on our service page have an MCP server developed.